import numpy as np

checkpoint_models_path = '../Models/DIM/'
train_log_path = '../Logs/DIM/'

input_mean = np.array([0.485, 0.456, 0.406])
input_std = np.array([0.229, 0.224, 0.225])

full_w, full_h = 384, 288
img_rows, img_cols = 288, 288
batch_size = 10
epochs = 1000
patience = 50
num_samples = 51040
num_train_samples = 48000
# num_samples - num_train_samples
num_valid_samples = 3040
unknown_code = 128
epsilon = 1e-6
epsilon_sqr = epsilon ** 2

##############################################################
# Set your paths here

# path to provided foreground images
fg_path = '../Datasets/DIM/RGBD/'

# path to provided alpha mattes
a_path = '../Datasets/DIM/ALPHA/'

# Path to background images (MSCOCO)
bg_path = 'data/bg/'

# Path to folder where you want the composited images to go
out_path = 'data/merged/'

##############################################################
